(Q41641386)

English

Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition

scientific article published on December 2014

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scholarly article

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Europe PubMed Central

PubMed publication ID

6 October 2017

Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition (English)

1 reference

Europe PubMed Central

PubMed publication ID

6 October 2017

7

1 reference

Europe PubMed Central

PubMed publication ID

6 October 2017

author name string

Pan-Pan Zhu

1

1 reference

Europe PubMed Central

PubMed publication ID

6 October 2017

Wen-Chao Li

2

1 reference

Europe PubMed Central

PubMed publication ID

6 October 2017

Zhe-Jin Zhong

3

1 reference

Europe PubMed Central

PubMed publication ID

6 October 2017

En-Ze Deng

4

1 reference

Europe PubMed Central

PubMed publication ID

6 October 2017

Hui Ding

5

1 reference

Europe PubMed Central

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6 October 2017

Wei Chen

6

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Europe PubMed Central

PubMed publication ID

6 October 2017

publication date

1 December 2014

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Europe PubMed Central

PubMed publication ID

6 October 2017

Molecular BioSystems

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Europe PubMed Central

PubMed publication ID

6 October 2017

11

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Europe PubMed Central

PubMed publication ID

6 October 2017

2

1 reference

Europe PubMed Central

PubMed publication ID

6 October 2017

558-563

1 reference

Europe PubMed Central

PubMed publication ID

6 October 2017

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Recent progress in protein subcellular location prediction

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Prediction of subcellular protein localization based on functional domain composition.

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Prediction of protein subcellular localization by support vector machines using multi-scale energy and pseudo amino acid composition

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Predict mycobacterial proteins subcellular locations by incorporating pseudo-average chemical shift into the general form of Chou's pseudo amino acid composition

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WoLF PSORT: protein localization predictor

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Identification of the subcellular localization of mycobacterial proteins using localization motifs.

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21 January 2018

Support Vector Machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary information and motifs

1 reference

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21 January 2018

Prediction of subcellular location of mycobacterial protein using feature selection techniques.

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iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition

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iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition

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Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection.

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iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition.

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iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition

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iDNA-Prot|dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid composition

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Some remarks on protein attribute prediction and pseudo amino acid composition

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UniProt Knowledgebase: a hub of integrated protein data

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PISCES: a protein sequence culling server

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iLoc-Euk: a multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins

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21 January 2018

iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites.

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iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites.

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iLoc-Gpos: a multi-layer classifier for predicting the subcellular localization of singleplex and multiplex Gram-positive bacterial proteins

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A multi-label classifier for predicting the subcellular localization of gram-negative bacterial proteins with both single and multiple sites

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iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites

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Tripeptide motifs in biology: targets for peptidomimetic design.

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Tripeptide analysis of protein structures

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A novel method for predicting protein subcellular localization based on pseudo amino acid composition

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A metabolomics approach to characterise and identify various Mycobacterium species

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1 reference

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Identifiers

10.1039/C4MB00645C

1 reference

Europe PubMed Central

PubMed publication ID

6 October 2017

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https://api.fatcat.wiki/v0/release/5vxdijbiv5cbtgpgmngi6wk67i

24 November 2022

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Europe PubMed Central

PubMed publication ID

6 October 2017

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